This is a drowsiness detection model that was made by fine-tuning the ultralytics yolov5 model on my custom data.
To create a drowsiness detection model, fine-tune the YOLOv5 model on custom labeled data with drowsy and awake states. Prepare and annotate images, configure YOLOv5, train the model, evaluate performance, and deploy for real-time detection in applications like driver safety and workplace monitoring.
This is a very useful and easy tool to use. First, we need to create labels such as "awake" and "drowsy" by drawing boxes around the face, giving them a label, and saving the file in Yolo format, as we are using the Yolo model.
### This is during the training of the model
